Listening to Trainee Concerns and Suggestions During COVID-19: a Report from the Canadian Consortium on Neurodegeneration in Aging (CCNA)
Bibliographic record
Abstract
Background: , 2020 to identify the challenges faced by CCNA trainees because of the pandemic and how to best support trainees in response to those challenges. Methods: Graduate students and postdoctoral researchers working under the supervision of CCNA investigators (n=113) were invited to complete a web-based survey of 13 questions. Trainees were asked questions about the impact of COVID-19 on their research activities, degree progression, funding status, and suggestions for support from the T&CB Program during the COVID-19 pandemic. Results: A total of 41 trainees responded to the survey (response rate: 36.3%); 83% of respondents reported that they experienced changes to their research activities as a result of COVID-19, and 50% anticipated that their degree completion would be delayed. Respondents requested information from the T&CB Program on funding for non-COVID-19 projects, alternative datasets, and short educational workshops. Conclusion: The majority of CCNA trainees surveyed experienced significant changes to their research activities as a result of the COVID-19 pandemic. The T&CB Program responded by switching to online programming and facilitating remote research. Further engagement with trainees is needed to ensure continued progress of research in age-related neurodegenerative disease in Canada post-pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".